Systems and Methods for Remote Patient Screening and Triage

ABSTRACT

A system for short-term screening and triage of a subject includes a smart device in the subject&#39;s possession and an application on the smart device programmed to provide instructions to the subject to start a screening procedure, the application programmed with multiple screening procedures, and record sensor data obtained from a sensor located in the smart device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalApplication Patent Ser. No. 63/003,551, filed Apr. 1, 2020, the entiredisclosure of which is hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates to remote biosignal monitoring of a subject,cardiac, and respiration monitoring being non-limiting examples. Moreparticularly, the disclosure pertains to systems and methods forshort-term and long-term patient screening with symptoms of a disease,bacterial or viral infections, cardiac related complications orrespiratory related complications as examples. The disclosure furtherrelates to using such systems to triage patients in order to determinethe risk level and priority for further evaluation.

BACKGROUND

Traditionally, an office or clinic visit is required for monitoring,diagnosis and evaluation of patients who might present symptoms ofinfection, or cardio-respiratory complications. This process risksincreased exposure of the public as well as the clinical team and mayoverload the in-patient hospital system capacity at times of crisis orpandemic in addition soldiers in the field may not have caregivers inthe vicinity. Currently, there are too many patients that requiremonitoring for the existing healthcare infrastructure but there are nohigh volume accurate remote monitoring tools available that can beeasily used or deployed without physical access to a caregiver. Unlikepersistent condition, paroxysmal conditions with sudden or intermittentonset require an at home screening solution that can be used immediatelyand continuously.

SUMMARY

Disclosed herein are systems that use optical, audio, radio, sensorssuch as accelerometer, gyroscope, pressure, load, weight, force, motionor vibration to capture the mechanical vibrations of the body as well asphysiological movements of heart and lungs and translate that intobiosignal information that can be used for screening and identifyingdisease conditions. The systems and methods presented here can be usedby a subject when experiencing symptoms of a complication or condition,or can be used when instructed by a physician in a telehealthapplication.

The systems are used for short-term and long-term screening. Short-termscreening systems can include cell phones, tablets, wearable watches orany accessory available to the patient that has one or more sensors thatcan capture the mechanical vibrations of the body, heart and lungs suchas accelerometer, gyroscope, pressure, load, weight, force, motion orvibration. Such devices can be placed near the source of body'sphysiological sources such as heart and lung, including but not limitedto placement on the chest, abdomen, side, back, or the like. Long-termscreening systems can include installable sensors into the legs or underthe legs of the bed, which can capture the mechanical vibrations of thebody, heart and lungs such as accelerometer, gyroscope, pressure, load,weight, force, motion or vibration. Short-term screening is intended forlimited duration tests (seconds to minutes) whereas long-term screeningcan be used continuously for any duration (seconds, minutes, days,months, etc). The short-term and long-term screening systems can workindependently or can be synchronized and work in unison to exchangedata, for example, subject's historical trend data or baseline data.

The systems can be used as a patient triage tool to help assess thedegree of urgency. The systems can include a smart phone appinstallation to enable screening and evaluating the patient's status.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIGS. 1A-1D illustrate an example system for short-term screening andtriage using a subject's smart device, as disclosed herein.

FIG. 1E is a set of data streams recorded during a short-term screeningusing a subject's smart device, as disclosed herein.

FIG. 2A is a flow diagram of another example system for short-termscreening and triage as disclosed herein.

FIG. 2B is a flow diagram of an example system for short-term and longterm screening and triage as disclosed herein.

FIG. 2C is a system architecture for implementing short-term and longterm screening and triage.

FIG. 3 is a flow diagram of an example process to collect sensor data.

FIG. 4 is a flow diagram of an example process for short-term analysisof sensor data.

FIG. 5 is a flow diagram of an example process for short-term cardiacanalysis.

FIG. 6 is a flow diagram of an example process for short-termrespiratory analysis.

FIG. 7 is a flow diagram of an example process for short-term coughinganalysis.

FIG. 8 is a flow diagram of an example process for short-term screeningand triage based on machine learning classifiers.

DETAILED DESCRIPTION

Methods are disclosed to develop remote screening procedures and usesensor data from such systems to triage a subject's health status. Inimplementations, the systems can analyze cardiac information of thesubject and determine cardiac rhythm, morphology, and rate information.The cardiac rhythm, morphology, and rate information can be used toidentify the start or worsening of a cardiac condition such as atrialfibrillation, atrial flutter, ventricular fibrillation, ventricularflutter, bundle branch blocks, valve stenosis, myocardial ischemia, andsupraventricular tachycardia. In implementations, the systems cananalyze respiratory information of the subject and determine respirationrhythm and rate information. The respiration rhythm and rate informationcan be used to identify start or worsening of a respiratory conditionsuch as shortness of breath, apnea or hypopnea. In implementations, thesystems can analyze coughing information of the subject and determinecoughing rhythm and rate information. The coughing rhythm and rateinformation can be used to identify the start or worsening of a coughingcondition or breathing flow such as wheezing, rales, snoring andrhonchi. The systems can determine the severity of the condition, and/orchanges and trends of the condition. The systems can generate remotescreening reports. The systems can collect the data and the generatedreport can be stored, sent to a physician, be accessed by the physicianfor review, or analyzed using AI techniques.

In implementations, the screening system can be used to monitor thebody's response to bacterial or viral infection. In particular, thesensors data can be used to monitor symptoms which may be directly orindirectly related to the increase in the body's temperature. Suchsymptoms can include changes in respiration flow and depth, respirationrate, heart rate, heart rate variability, movement and agitation,weight, and fluid retention. The system can further create a baselinefor the patient, and continuously track and spot these changes overtime, helping them become aware of their body's immune system responseand enabling them, or a caregiver, to monitor their sickness.

In implementations, the screening system can be used in conjunction witha ventilator to remotely monitor the ventilator's efficacy. In addition,the system can be used with other sensors as a data hub to relay localdata from pulse oximeters, thermometers, blood pressure or other sensorsto a cloud enabling remote monitoring of these additional sensors forenhanced screening.

In implementations, the system will include bi-directional audio, text,and video to communicate with the patient.

In implementations, the screening system can also be used to screen forcardiovascular and autonomic indices. For example, the system can beused for in home stress testing where sensors data can be used tomonitor indices of heart rate variability to quantify dynamic autonomicmodulation or heart rate recovery.

In implementations, the system can be used to create events based on thevital's analysis. The event may be an audible tone or message sent tothe cloud for a critical condition. In implementations, the systemenables data convergence between the short-term and long-term monitoringsystems such that, for example, one system can use the historical datacollected by the other system to establish a baseline, or to use suchinformation to determine the progression of a disease or worsening of acondition.

In implementations, additional bed-based sensor data can be combined toremove or cancel out common mode or other noise sources. Mobile dataacquisition sensors can be used in conjunction with other monitoringsystems that have a fixed location so that data from different sourcesof data monitoring may be combined to increase total monitoring coveragewhen someone is mobile.

FIGS. 1A-1D illustrate an example system for short-term screening andtriage using a subject's smart device. FIG. 1A is a flow chart of anexample method 100 for using a screening and triage system using asubject's smart device. In implementations, the smart device can includecell phones, tablets, wearable watches, or any accessory available tothe subject that has one or more sensors that can capture the mechanicalvibrations of the body, heart, and lungs such as an accelerometer,gyroscope, pressure sensor, load sensor, weight sensor, force sensor,motion sensor, or vibration sensor. The figures illustrate a cell phoneas a non-limiting example.

A start of the screening can be triggered by the start of the symptomsof an illness or condition (for example, when the subject is not feelingwell) or can be initiated per the physician's request (101). The subjectwill receive screening instructions (102). In implementations, this canbe given by the physician. In implementations, the instructions can beprovided by an app installed on the smartphone or other mobile device.Instructions are condition specific; therefore, the screening procedurefor an infection may be different from the screening procedure for acardiac condition. FIGS. 1B-1D are example of screening instructions(103) where an app instructs the subject to perform screeningprocedures. In implementations, the app can instruct the subject to layin bed, remain stationary for a given time (a counter can be used todisplay the time or to play a countdown tone) as shown in FIG. 1B. Inimplementations, the app can instruct the subject to place the phone onhis/her chest at different locations as shown in FIG. 1C. Inimplementations, the app can instruct the subject to lay on his/her sideas shown in FIG. 1D. In implementations, other placements are possible,for example on the back, stomach, abdomen, and the like. The app recordsthe sensors data, analyzes the sensors data (104) and generates a healthreport (105). In implementations, the report can include physiologicalmeasurements, for example, heart rate, respiration rate, heart ratevariability, etc. In implementations, the report can further include alist of identified or suspected issues, and a degree of urgency(severity level). In implementations, the app can send the data to thephysician or caregiver, or can suggest follow-up tests using the same ora different system.

FIG. 1E are graphs of data streams recorded during a short-termscreening using a subject's smart device. FIG. 1E shows example datastreams when the subject is laying in bed, with his smartphone placed onhis chest (as shown in FIG. 1B) and a smart watch worn on his wrist. The(X,Y,Z) data streams from the phone's accelerometer are plotted in thetop panel. The (X,Y,Z) data streams from the watch's accelerometer areplotted in the mid panel. The (X,Y,Z) data streams from the phone'sgyroscope are plotted in the bottom panel. The phone's accelerometerdata captures both cardiac and respiration activity. In this example,the respiration signal is strongly seen in X and Y components whereasthe heart activity is strongest in Z direction. The watch'saccelerometer data doesn't capture the respiration signal. The watchonly captures the cardiac activity. The gyroscope data captures bothrespiration and cardiac activity. The impact of a breath hold is visiblein data streams where respiration is recorded (phone accelerometer andgyroscope). The coughing episodes are visible in all recorded datastreams.

FIG. 2A is a flow diagram of an example of a method 200 for short-termscreening and triage using the subject's smart device. Inimplementations, the smart device can include cell phones, tablets,wearable watches or any accessory available to the patient that has oneor more sensors that can capture the mechanical vibrations of the body,heart and lungs such as accelerometer, gyroscope, pressure, load,weight, force, motion or vibration.

A start of the screening can be triggered by the start of the symptomsof an illness or condition (for example, when the subject is not feelingwell) or can be initiated per the physician's request (201). After thescreening starts, sensor data is obtained from the sensors (202). Thesensor data is analyzed (203). Subject's conditions are identified usingthe analyzed metrics (204). In implementations, to quantify a body'sresponse to bacterial or viral infection, changes in respiration flowand depth, respiration rate, heart rate, heart rate variability,movement and agitation, weight, and fluid retention are analyzed. Valuesoutside a normal range may define an out-of-the-norm condition or if thesystem has access to a baseline for the patient, abrupt changes comparedto the baseline may be detected as an out-of-the-norm condition. Inimplementations, normal range or out-of-the norm condition can berelative to data representing a general population. Once a condition hasbeen identified, a determination is made as to whether immediate actionis needed (208). An immediate action is taken if necessary (209). Inimplementations, immediate action can be a notification to the patient,a notification to the patient's physician, a call to a health center, orthe like. If no immediate action is required, a determination is made asto whether a follow-up test is required to confirm the results or toprovide new insights (210). If a follow-up is needed, additional or newsensor data is collected and the process starts anew. Otherwise, thescreening process is terminated (211). In implementations, the sensorsdata, analyzed data, and identified data can be stored locally in alocal database 206 or in a cloud database 207 for future access (205).

FIG. 2B shows an example method 220 for short-term and long-termscreening and triage. Short-term screening can use subject's smartdevice. In implementations, the smart device can include cell phones,tablets, wearable watches or any accessory available to the patient thathas one or more sensors that can capture the mechanical vibrations ofthe body, heart and lungs such as accelerometer, gyroscope, pressure,load, weight, force, motion or vibration. Long-term screening can useinstallable sensors into the legs or under the legs of a bed (an exampleof a substrate on which a subject can be positioned on), which cancapture the mechanical vibrations of the body, heart and lungs such asaccelerometer, gyroscope, pressure, load, weight, force, motion, orvibration. If the short-term screening determines that a long-termscreening is required (229), the system can recommend adding a long-termscreening system. The long-term screening can be added for enhancedcontinuous biosignal tracking.

Exchange data process 231 enables data exchange between the short-termand long-term screening, where trend data (i.e. subject's baseline dataand historical data) can be accessed by either process. The exchangedata process 231 also enables the long-term screening to have access tothe short-term screening sensors data to be synchronized and added tothe data stream sets for enhanced monitoring. In implementations, datafrom obtain sensor data 222, obtain sensor data 232, store data 226including the local and cloud based storages, and store data 236including the local and cloud based storages can be input into theexchange data process 231. In implementations, the exchange data process231 outputs data to obtain trend data 224 and obtain trend data 234.That is, both initial and processed short-term data and long-term datacan be exchanged between the short-term and long-term screening.

FIG. 2C is a system 250 and system architecture for implementingshort-term and long term screening and triage. The system 250 includesone or more devices 260 which are connected to or in communication with(collectively “connected to”) a computing platform 270. In animplementation, a machine learning training platform 280 may beconnected to the computing platform 270. In an implementation, users mayaccess the data via a connected device 290, which may receive data fromthe computing platform 270 or the device 260. The connections betweenthe one or more devices 260, the computing platform 270, the machinelearning training platform 280, and the connected device 290 can bewired, wireless, optical, combinations thereof and/or the like. Thesystem 250 is illustrative and may include additional, fewer ordifferent devices, entities and the like which may be similarly ordifferently architected without departing from the scope of thespecification and claims herein. Moreover, the illustrated devices mayperform other functions without departing from the scope of thespecification and claims herein.

In implementations, the system 250, the sensors and the data processing,for example, can be as described in U.S. patent application Ser. No.16/777,385, filed on Jan. 30, 2020, U.S. patent application Ser. No.16/595,848, filed Oct. 8, 2019, and U.S. Provisional Application PatentSer. No. 62/804,623, filed Feb. 12, 2019 (collectively the“Applications”) the entire disclosures of which are hereby incorporatedby reference.

In implementations, the device 260 can include one or more sensors 261,a controller 262, a database 263, and a communications interface 264. Inan implementation, the device 261 can include a classifier 265 forapplicable and appropriate machine learning techniques as describedherein. The one or more sensors 261 can detect and capture vibration,pressure, force, weight, presence, and motion sensor data related to asubject.

In implementations, the controller 262 can apply the processes andalgorithms described herein with respect to FIGS. 1A, 2A, 2B, and 4-8 tothe sensor data to determine short-term and long-term screeningbiosignal information and data as described herein. In implementations,the classifier 265 can apply the processes and algorithms describedherein with respect to FIGS. 1A, 2A, 2B, and 4-8 to the sensor data todetermine short-term and long-term screening biosignal information anddata. In implementations, the classifier 265 may be implemented by thecontroller 262. In implementations, the captured sensor data and theshort-term and long-term screening biosignal information and data can bestored in the database 263. In an implementation, the captured sensordata and the short-term and long-term screening biosignal informationand data can be transmitted or sent via the communication interface 264to the computing platform 270 for processing, storage, and/orcombinations thereof. The communication interface 264 can be anyinterface and use any communications protocol to communicate or transferdata between origin and destination endpoints. In an implementation, thedevice 260 can be any platform or structure which uses the one or moresensors 261 to collect the data from a subject(s) for use by thecontroller 262 and/or computing platform 270 as described herein. Thedevice 260 and the elements therein may include other elements which maybe desirable or necessary to implement the devices, systems, and methodsdescribed herein. However, because such elements and steps are wellknown in the art, and because they do not facilitate a betterunderstanding of the disclosed embodiments, a discussion of suchelements and steps may not be provided herein.

In implementations, the computing platform 270 can include a processor271, a database 272, and a communication interface 273. Inimplementations, the computing platform 270 may include a classifier 274for applicable and appropriate machine learning techniques as describedherein. The processor 271 can obtain the sensor data from the sensors261 or the controller 262 and can apply the processes and algorithmsdescribed herein with respect to FIGS. 1A, 2A, 2B, and 4-8 to the sensordata to determine short-term and long-term screening biosignalinformation and data as described herein. In an implementation, theprocessor 271 can obtain the short-term and long-term screeningbiosignal information and data as described herein from the controller262 to store in database 272 for temporal and other types of analysis.In an implementation, the classifier 274 can apply the processes andalgorithms described herein with respect to FIGS. 1A, 2A, 2B, and 4-8 tothe sensor data to determine short-term and long-term screeningbiosignal information and data as described herein. The classifier 274can apply classifiers to the sensor data to determine short-term andlong-term screening biosignal information and data as described hereinvia machine learning. In an implementation, the classifier 274 may beimplemented by the processor 271. In an implementation, the capturedsensor data and the short-term and long-term screening biosignalinformation and data can be stored in the database 272. Thecommunication interface 273 can be any interface and use anycommunications protocol to communicate or transfer data between originand destination endpoints. In an implementation, the computing platform270 may be a cloud-based platform. In an implementation, the processor271 can be a cloud-based computer or an off-site controller. Thecomputing platform 270 and elements therein may include other elementswhich may be desirable or necessary to implement the devices, systems,and methods described herein. However, because such elements and stepsare well known in the art, and because they do not facilitate a betterunderstanding of the disclosed embodiments, a discussion of suchelements and steps may not be provided herein.

In an implementation, the machine learning training platform 280 canaccess and process sensor data to train and generate classifiers. Theclassifiers can be transmitted or sent to the classifier 265 or to theclassifier 274.

In FIG. 2B, sensor data is obtained from sensors (232). Inimplementations, the sensor data can be analyzed, for example, similarlyas shown in the Applications (233). Instantaneous or near instantaneousdata from short term processing via exchange data process 231 isobtained by the long term processing (234). Conditions related to thesubject are identified using the analyzed data and the obtained data(235). The identified conditions and data are stored in local or cloudbased storage (236). As stated herein, the identified conditions anddata are also input the exchange data process 231. A determination ismade as to whether the identified conditions require immediate action(237). If not, nominal long term processing continues. If immediateaction is needed, then a responsive action is performed (238).

In FIG. 2B, sensor data is obtained from sensors (221). Inimplementations, the sensor data can be analyzed, for example, similarlyas shown in the Applications (223). Trend data from long term processingvia exchange data process 231 is obtained by the short term processing(224). Conditions related to the subject are identified using theanalyzed data and the obtained data (225). The identified conditions anddata are stored in local or cloud based storage (226). As stated herein,the identified conditions and data are also input the exchange dataprocess 231. A determination is made as to whether the identifiedconditions require immediate action (227). If immediate action isneeded, then a responsive action is performed (228). If not needed,determine if long term processing is needed (229). If yes, perform longterm processing. If not needed, determine if short term processing isneeded (230). If not needed, current short term processing terminates.If yes, obtain data and perform another short term processing.

FIG. 3 is a processing pipeline 300 for obtaining sensor data such as,but not limited to, accelerometer, gyroscope, pressure, load, weight,force, motion or vibration. An analog sensors data stream 302 isreceived from the sensors 301. A digitizer 303 digitizes the analogsensors data stream into a digital sensors data stream 304. A framer 305generates digital sensors data frames 306 from the digital sensors datastream 304 which includes all the digital sensors data stream valueswithin a fixed or adaptive time window. The processing pipeline 300shown in FIG. 3 is illustrative and can include any, all, none or acombination of the blocks or modules shown in FIG. 3. The processingorder shown in FIG. 3 is illustrative and the processing order may varywithout departing from the scope of the specification or claims.

FIG. 4 is a pre-processing pipeline 400 for processing the sensor data.The pre-processing pipeline 400 processes digital sensor data frames401. A noise reduction unit 402 removes or attenuates noise sources thatmight have the same or different level of impact on each sensor. Thenoise reduction unit 402 can use a variety of techniques including, butnot limited to, subtraction, combination of the input data frames,adaptive filtering, wavelet transform, independent component analysis,principal component analysis, and/or other linear or nonlineartransforms. A signal enhancement unit 403 can improve the signal tonoise ratio of the input data. The signal enhancement unit 403 can beimplemented as a linear or nonlinear combination of input data frames.For example, the signal enhancement unit 403 may combine the signaldeltas to increase the signal strength for higher resolution algorithmicanalysis. Subsampling units 404, 405 and 406 sample the digital enhancedsensor data and can include downsampling, upsampling or resampling. Thesubsampling can be implemented as a multi-stage sampling or multi-phasesampling, and can use the same or different sampling rates for cardiac407, respiratory 408, and coughing 409 analysis. The processing ordershown in FIG. 4 is illustrative and the processing order may varywithout departing from the scope of the specification or claims.

FIG. 5 is an example process 500 for cardiac analysis 407 using thepre-processed and sub-sampled data 501. Filtering is used to removeunwanted components of the input sensor data or to keep contents thatare useful for cardiac processing (502). In implementations, thefiltering can an infinite impulse response (IIR) filter, finite impulseresponse (IIR) filter, or a combination thereof. Filter can be low pass,high pass, bandpass, bandstop, notch or a combination of these. Inimplementations, the filtering can include sources from other sensors toremove common mode or other noise and can use adaptive filteringtechniques to remove unwanted signals. The filtered sensor data istransformed to enhance cardiac components by modeling the input signalas a collection of waveforms of a particular form (sinusoids for theFourier transform, mother wavelets for the wavelet transforms, and/orperiodic basis functions for the periodicity transforms) (503). Inimplementations, the process can be a Fourier transform, wavelettransform, cosine transform or a math operation such asroot-mean-square, absolute, moving average, moving median, etc.

Envelope detection is performed on the transformed sensor data, whichtakes a relatively high-frequency amplitude modulated signal as inputand provides an output which is equivalent to the outline of the inputdata described by connecting all the local peaks in this signal (512).In implementations, envelope detection can use a low pass filter, aHilbert transform or other envelope detection methods. Peak detection isperformed to find local maximum and minimum points of the input signal(513). In implementations, peak detection can return all peaks, valleysor only the most dominant ones.

Correlation analysis is performed to measure the strength ofrelationship between different segments of the input signal using linearand nonlinear methods (504). The correlation analysis and peak locationscan be used to identify individual beats in the input signal (505). Theidentified individual beats are enhanced (506). In implementations, thiscan include applying a window, a factor, or a transform to enhancespecific characteristics of the signal. Time domain, frequency domain,or time frequency domain analysis can be performed to determine theheart rate using the enhanced individual beats (507). Time domain,frequency domain, or time frequency domain analysis can be performed todetermine the heart rate variability metrics using the enhancedindividual beats (508). In implementations, the heart rate variabilitymetrics can include SDNN, RMSSD, PNN50, LF, HF and LF/HF indices. Timedomain, frequency domain, or time frequency domain analysis can beperformed to determine heartbeat components (509). For the cardiacsignal, beat components can be P, Q, R, S and T waveforms, oratria/ventricular depolarization and repolarization. Irregular rate orrhythm can be detected in the cardiac data using the HR, HRV, beatcomponents, and subject's trend data 510 (i.e. baseline and historicaldata) (511).

The processing order shown in FIG. 5 is illustrative and the processingorder may vary without departing from the scope of the specification orclaims.

FIG. 6 is an example process for respiratory analysis 408 using thepre-processed and sub-sampled data 601. Filtering is used to removeunwanted components of the input sensor data or to keep contents thatare useful for respiration processing (602). In implementations, thefilter can use an IIR, FIR, or a combination thereof. Inimplementations, the filter can be low pass, high pass, bandpass,bandstop, notch, or a combination thereof. In implementations, thefilter may include sources from other sensors to remove common mode orother noise and can use adaptive filtering techniques to remove unwantedsignals. The filtered data is transformed to enhance respiratorycomponents by modeling the input signal as a collection of waveforms ofa particular form (sinusoids for the Fourier transform, mother waveletsfor the wavelet transforms, periodic basis functions for the periodicitytransforms) (603). The transform can be a Fourier transform, wavelettransform, cosine transform, or a math operation such asroot-mean-square, absolute, moving average, moving median, etc. Peakdetection is performed to find local maximum and minimum points of theinput signal (605). In implementations, the peak detection can returnall peaks, valleys, or only the most dominant ones.

Correlation analysis measures the strength of relationship betweendifferent segments of the input signal using linear and nonlinearmethods (604). The correlation analysis and peak locations can be usedto identify individual breaths in the input signal (606). The identifiedindividual beats are enhanced (607). In implementations, this caninclude applying a window, or a factor, or a transform to enhancespecific characteristics of the signal.

Time domain, frequency domain, or time frequency domain analysis can beused to determine the respiration rate using the enhanced individualbreaths (608). Time domain, frequency domain, or time frequency domainanalysis can be used to determine the respiration rate variabilitymetrics using the enhanced individual breaths (609). In implementation,the respiration rate variability metrics can include DNN, RMSSD, PNN50,LF, HF and LF/HF indices. Time domain, frequency domain, or timefrequency domain analysis can be used to determine breath components(610). For a respiratory signal, breath components can be inhale(inspiration) and exhale (expiration). Irregular rate or rhythm can beidentified in the respiration data using the RR, RRV, breath componentsand subject's trend data 611 (i.e. baseline and historical data) (612).

The processing order shown in FIG. 6 is illustrative and the processingorder may vary without departing from the scope of the specification orclaims.

FIG. 7 is an example process for coughing analysis 409 using thepre-processed and sub-sampled data 701. Filtering is used to removeunwanted components of the input sensor data or to keep contents thatare useful for coughing processing (702). In implementations, the filtercan be an IIR, FIR or a combination of the two. In implementations, thefilter can be low pass, high pass, bandpass, bandstop, notch, or acombination thereof. In implementations, the filtering may includesources from other sensors to remove common mode or other noise and canuse adaptive filtering techniques to remove unwanted signals. Thefiltered sensor data is transformed to enhance coughing components bymodeling the input signal as a collection of waveforms of a particularform (sinusoids for the Fourier transform, mother wavelets for thewavelet transforms, periodic basis functions for the periodicitytransforms) (703). In implementations, the transform can be a Fouriertransform, wavelet transform, cosine transform or a math operation suchas root-mean-square, absolute, moving average, moving median, etc.Envelope detection can be performed to take a relatively high-frequencyamplitude modulated signal as input and provide an output which isequivalent to the outline of the input data described by connecting allthe local peaks in this signal (704). In implementations, envelopedetection can use a low pass filter, a Hilbert transform or otherenvelope detection methods. Patterns in the processed sensor data can bedetected that match the morphology or spectral signature of coughing(705).

Variation analysis can be performed to measure the level of change inthe data compared to be baseline (706). In implementations, this can bedone by estimating a standard deviation, coefficient of variation, andthe like. The variation analysis and cough signatures can be used toidentify individual coughing episodes in the input signal (707). Timedomain, frequency domain, or time frequency domain analysis can be usedto determine the coughing rate (708). Time domain, frequency domain, ortime frequency domain analysis can be used to determine the coughseverity (709). Irregular coughs can be determined using the cough rate,cough severity, and subject's trend data 710 (i.e., baseline andhistorical data) (711).

The processing order shown in FIG. 7 is illustrative and the processingorder may vary without departing from the scope of the specification orclaims.

FIG. 8 is an example process for short-term screening and triage basedon machine learning classifiers. A swim lane diagram 800 includesdevices 801 which include a first set of devices 806 and a second set ofdevices 807, a local database 802, a cloud server 803, a classifierfactory 804, and a configuration server 805.

The first set of devices 806 generate sensors data which are received(808) and stored (809) by the local database 802, and received by thecloud server 803. The cloud server 803 retrieves the sensor data (812)and the classifier factory 804 generates or retrains classifiers (814).The generated or retrained classifiers are stored by the classifierfactory 804 (815). The generated or retrained classifiers are used bythe classifier factory 804 to classify sensor data (816) andautomatically detect different arrhythmias, diseases or out-of-the-normconditions. The classified data is stored (813) and subjects trend datais stored (810). The configuration server 805 obtains the generated orretrained classifiers and generates an update for devices 801 (817). Inimplementations, the update can be an app update for the smart devicesor a software update for remote devices. The configuration server 805sends the update (818) to both the first set of devices 806 and to thesecond set of devices 807, where the second set of devices 807 may benew devices. The system can be used to provide new or updatedclassifiers to old devices (such as the first set of devices 806) asmore data input is available from more devices. The system can also beused to provide software updates with improved accuracy and can alsolearn personalized patterns and increase personalization of classifiersor data.

The processing order shown in FIG. 8 is illustrative and the processingorder may vary without departing from the scope of the specification orclaims.

In general, a system for at least short term screening and triage of asubject includes a smart device in the subject's possession; anapplication provisioned on the smart device, the application and thesmart device configured to provide instructions to the subject to starta screening procedure, the application programmed with multiplescreening procedures, record short term sensor data obtained from one ormore sensors located in the smart device, and compare the short termdata, trend data, and general population data for screening and triageof the subject.

In implementations, the application and the smart device furtherconfigured to analyze the short term sensor data, obtain trend data fromlong term screening, identify conditions based on analyzed short termsensor data and trend data, and perform an action responsive to theidentified conditions. In implementations, the action includes one ormore of: generating an audible tone for a critical condition, sending amessage to a cloud entity for a critical condition, sending the sensordata and identified conditions to an entity not the subject. Inimplementations, the application and the smart device further configuredto initiate a long term screening to generate the trend data responsiveto the identified conditions, wherein a long term screening systemincludes one or more sensors installed proximate to a substrate on whichthe subject is positioned, each sensor configured to capture mechanicalvibrations from actions of the subject relative to the substrate, themechanical vibrations indicative of biosignal information of thesubject. In implementations, the long term screening system furtherconfigured to access the short term sensor data and identifiedconditions. In implementations, the application and the smart devicefurther configured to initiate further short term screening responsiveto the identified conditions. In implementations, the application andthe smart device further configured to analyze cardiac information ofthe subject, determine cardiac rhythm and rate information from thecardiac information, determine the subject's health status from thecardiac rhythm and rate information, and identify onset or progressionof a cardiac condition. In implementations, the application and thesmart device further configured to: analyze respiratory information ofthe subject, determine respiration rhythm and rate information from therespiratory information, determine the subject's health status from therespiration rhythm and rate information, and identify onset orprogression of a respiratory condition. In implementations, theapplication and the smart device further configured to: analyze coughinginformation of the subject, determine coughing rhythm and rateinformation from the coughing information, determine the subject'shealth status from the coughing rhythm and rate information, andidentify onset or progression of a coughing condition or breathing flow.In implementations, the application and the smart device furtherconfigured to the application and the smart device further configured todetermine the severity or progression of the condition. Inimplementations, the smart device is one of a cell phone, tablet, smartwatch or accessory that has one or more of an accelerometer, gyroscope,pressure sensor, load sensor, weight sensor, force sensor, motionsensor, microphone, or vibration sensor. In implementations, thescreening procedure includes instructions for the subject to get in aposition, stay in the position for a defined time, and place the smartdevice at one or more positions on a body of the subject.

In general, a system for screening and triage of a subject includes asmart device provisioned with an application, collectively configuredto: instruct a subject to start a screening procedure, record short termsensor data obtained from at least one sensor located in the smartdevice, and identify a condition from the short term sensor data andobtained trend data, a substrate deployed with sensors, the sensorsconfigured to capture mechanical vibrations from actions of the subjectrelative to the substrate, the mechanical vibrations indicative ofbiosignal information of the subject, and a processor connected to thesensors, the processor configured to: capture sensor data from thesensors responsive to a smart device identified condition, identify acondition from the sensor data captured from the sensors and obtainedshort term sensor data, and perform an action based on an identifiedcondition.

In implementations, the application and the smart device furtherconfigured to: analyze the short term sensor data sensor data, obtaintrend data from storage associated with the processor, and perform anaction responsive to the smart device identified condition. Inimplementations, the application and the smart device further configuredto: transmit the short term sensor data sensor data to an entity foranalysis against trend data, obtain results of analysis, and perform anaction responsive to an entity identified condition. In implementations,the action responsive to the smart device identified condition or theaction responsive to an entity identified condition includes one or moreof: generating an audible tone for a critical condition, sending amessage to a cloud entity for a critical condition, sending the shortterm sensor data and identified conditions to an entity not the subject.In implementations, the application and the smart device furtherconfigured to initiate further smart device screening responsive to thesmart device identified condition. In implementations, the applicationand the smart device further configured to perform at least one of:analyze cardiac information of the subject, determine cardiac rhythm andrate information from the cardiac information, determine the subject'shealth status from the cardiac rhythm and rate information, and identifyonset or progression of a cardiac condition, or analyze respiratoryinformation of the subject, determine respiration rhythm and rateinformation from the respiratory information, determine the subject'shealth status from the respiration rhythm and rate information, andidentify onset or progression of a respiratory condition, or analyzecoughing information of the subject, determine coughing rhythm and rateinformation from the coughing information, determine the subject'shealth status from the coughing rhythm and rate information, andidentify onset or progression of a coughing condition or breathing flow.

In general, a method for at least short term screening and triaging of asubject, the method includes instructing a subject, via a smart device,to initiate a screening procedure, recording, by a sensor on the smartdevice, short term sensor data from the subject as he/she follows thescreening procedure, analyzing the short term sensor data, obtainingtrend data from a long term screening device, identifying a subjectcondition based on analyzed short term sensor data and the trend data,and performing an action responsive to an identified condition.

In implementations, the method includes initiating capturing of sensordata at the long term screening device responsive to an identifiedcondition. In implementations, the method includes analyzing long termscreening device sensor data, obtaining smart device data, identifying asubject condition based on analyzed long term screening device sensordata, general population data, and the short term sensor data, andperforming an action responsive to a long term screening deviceidentified condition. In implementations, the method includes sendingshort term sensor data, smart device identified condition, long termscreening device sensor data, and long term screening device identifiedcondition, respectively, to at least an entity not the subject. Inimplementations, the method includes initiating additional smart devicescreening responsive to the identified condition.

While the disclosure has been described in connection with certainembodiments, it is to be understood that the disclosure is not to belimited to the disclosed embodiments but, on the contrary, is intendedto cover various modifications and equivalent arrangements includedwithin the scope of the appended claims, which scope is to be accordedthe broadest interpretation so as to encompass all such modificationsand equivalent structures as is permitted under the law.

What is claimed is:
 1. A system for at least short term screening andtriage of a subject, the system comprising: a smart device in thesubject's possession; and an application provisioned on the smartdevice, the application and the smart device configured to: provideinstructions to the subject to start a screening procedure, theapplication programmed with multiple screening procedures; record shortterm sensor data obtained from one or more sensors located in the smartdevice; and compare the short term data, trend data, and generalpopulation data for screening and triage of the subject.
 2. The systemof claim 1, wherein the application and the smart device furtherconfigured to: analyze the short term sensor data; obtain trend datafrom long term screening; identify conditions based on analyzed shortterm sensor data and trend data; and perform an action responsive to theidentified conditions.
 3. The system of claim 2, wherein the actionincludes one or more of: generating an audible tone for a criticalcondition; sending a message to a cloud entity for a critical condition;sending the sensor data and identified conditions to an entity not thesubject.
 4. The system of claim 2, wherein the application and the smartdevice further configured to: initiate a long term screening to generatethe trend data responsive to the identified conditions, wherein a longterm screening system includes one or more sensors installed proximateto a substrate on which the subject is positioned, each sensorconfigured to capture mechanical vibrations from actions of the subjectrelative to the substrate, the mechanical vibrations indicative ofbiosignal information of the subject.
 5. The system of claim 4, whereinthe long term screening system further configured to access the shortterm sensor data and identified conditions.
 6. The system of claim 2,wherein the application and the smart device further configured to:initiate further short term screening responsive to the identifiedconditions.
 7. The system of claim 2, wherein the application and thesmart device further configured to: analyze cardiac information of thesubject; determine cardiac rhythm and rate information from the cardiacinformation; determine the subject's health status from the cardiacrhythm and rate information; and identify onset or progression of acardiac condition.
 8. The system of claim 2, wherein the application andthe smart device further configured to: analyze respiratory informationof the subject; determine respiration rhythm and rate information fromthe respiratory information; determine the subject's health status fromthe respiration rhythm and rate information; and identify onset orprogression of a respiratory condition.
 9. The system of claim 2,wherein the application and the smart device further configured to:analyze coughing information of the subject; determine coughing rhythmand rate information from the coughing information; determine thesubject's health status from the coughing rhythm and rate information;and identify onset or progression of a coughing condition or breathingflow.
 10. The system of claim 2, wherein the application and the smartdevice further configured to:
 11. The system of claim 2, wherein theapplication and the smart device further configured to determine theseverity or progression of the condition.
 12. The system of claim 1,wherein the smart device is one of a cell phone, tablet, smart watch oraccessory that has one or more of an accelerometer, gyroscope, pressuresensor, load sensor, weight sensor, force sensor, motion sensor,microphone, or vibration sensor.
 13. The system of claim 1, wherein thescreening procedure includes instructions for the subject to get in aposition, stay in the position for a defined time, and place the smartdevice at one or more positions on a body of the subject.
 14. A systemfor screening and triage of a subject, the system comprising: a smartdevice provisioned with an application, collectively configured to:instruct a subject to start a screening procedure; record short termsensor data obtained from at least one sensor located in the smartdevice; and identify a condition from the short term sensor data andobtained trend data; a substrate deployed with sensors, the sensorsconfigured to capture mechanical vibrations from actions of the subjectrelative to the substrate, the mechanical vibrations indicative ofbiosignal information of the subject; and a processor connected to thesensors, the processor configured to: capture sensor data from thesensors responsive to a smart device identified condition; identify acondition from the sensor data captured from the sensors and obtainedshort term sensor data; and perform an action based on an identifiedcondition.
 15. The system of claim 14, wherein the application and thesmart device further configured to: analyze the short term sensor datasensor data; obtain trend data from storage associated with theprocessor; and perform an action responsive to the smart deviceidentified condition.
 16. The system of claim 15, wherein theapplication and the smart device further configured to: transmit theshort term sensor data sensor data to an entity for analysis againsttrend data; obtain results of analysis; and perform an action responsiveto an entity identified condition.
 17. The system of claim 16, whereinthe action responsive to the smart device identified condition or theaction responsive to an entity identified condition includes one or moreof: generating an audible tone for a critical condition; sending amessage to a cloud entity for a critical condition; sending the shortterm sensor data and identified conditions to an entity not the subject.18. The system of claim 14, wherein the application and the smart devicefurther configured to: initiate further smart device screeningresponsive to the smart device identified condition.
 19. The system ofclaim 14, wherein the application and the smart device furtherconfigured to perform at least one of: analyze cardiac information ofthe subject; determine cardiac rhythm and rate information from thecardiac information; determine the subject's health status from thecardiac rhythm and rate information; and identify onset or progressionof a cardiac condition; or analyze respiratory information of thesubject; determine respiration rhythm and rate information from therespiratory information; determine the subject's health status from therespiration rhythm and rate information; and identify onset orprogression of a respiratory condition; or analyze coughing informationof the subject; determine coughing rhythm and rate information from thecoughing information; determine the subject's health status from thecoughing rhythm and rate information; and identify onset or progressionof a coughing condition or breathing flow.
 20. A method for at leastshort term screening and triaging of a subject, the method comprising:instructing a subject, via a smart device, to initiate a screeningprocedure; recording, by a sensor on the smart device, short term sensordata from the subject as he/she follows the screening procedure;analyzing the short term sensor data; obtaining trend data from a longterm screening device; identifying a subject condition based on analyzedshort term sensor data and the trend data; and performing an actionresponsive to an identified condition.
 21. The method of claim 20,further comprising: initiating capturing of sensor data at the long termscreening device responsive to an identified condition.
 22. The methodof claim 21, further comprising: analyzing long term screening devicesensor data; obtaining smart device data; identifying a subjectcondition based on analyzed long term screening device sensor data,general population data, and the short term sensor data; and performingan action responsive to a long term screening device identifiedcondition.
 23. The method of claim 22, further comprising: sending shortterm sensor data, smart device identified condition, long term screeningdevice sensor data, and long term screening device identified condition,respectively, to at least an entity not the subject.
 24. The method ofclaim 20, further comprising: initiating additional smart devicescreening responsive to the identified condition.